



"""simple docstring"""

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class     lowerCAmelCase__						(	_UpperCAmelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= OpenAIGPTTokenizer
	_SCREAMING_SNAKE_CASE	:		str  						= OpenAIGPTTokenizerFast
	_SCREAMING_SNAKE_CASE	:		Dict  						= True
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= False





	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					SCREAMING_SNAKE_CASE						:							Any							=     [
					    'l',
					    'o',
					    'w',
					    'e',
					    'r',
					    's',
					    't',
					    'i',
					    'd',
					    'n',
					    'w</w>',
					    'r</w>',
					    't</w>',
					    'lo',
					    'low',
					    'er</w>',
					    'low</w>',
					    'lowest</w>',
					    'newer</w>',
					    'wider</w>',
					    '<unk>',
					]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     dict(zip(_SCREAMING_SNAKE_CASE     ,       range(len(_SCREAMING_SNAKE_CASE    )    )    )    )
					SCREAMING_SNAKE_CASE						:							Dict							=     ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

					SCREAMING_SNAKE_CASE						:							str							=     os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['vocab_file']    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['merges_file']    )
					with open(self.vocab_file     ,       'w'    ) as fp:
									fp.write(json.dumps(_SCREAMING_SNAKE_CASE    )    )
					with open(self.merges_file     ,       'w'    ) as fp:
									fp.write('\n'.join(_SCREAMING_SNAKE_CASE    )    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Any    )						->	List[Any]:





					"""simple docstring"""


					return "lower newer", "lower newer"





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     OpenAIGPTTokenizer(self.vocab_file     ,       self.merges_file    )

					SCREAMING_SNAKE_CASE						:							List[Any]							=     'lower'
					SCREAMING_SNAKE_CASE						:							Tuple							=     ['low', 'er</w>']
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     tokenizer.tokenize(_SCREAMING_SNAKE_CASE    )
					self.assertListEqual(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							int							=     tokens + ['<unk>']
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     [14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=15    )						->	Any:





					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

													# Simple input
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'This is a simple input'
													SCREAMING_SNAKE_CASE						:							Tuple							=     ['This is a simple input 1', 'This is a simple input 2']
													SCREAMING_SNAKE_CASE						:							str							=     ('This is a simple input', 'This is a pair')
													SCREAMING_SNAKE_CASE						:							List[str]							=     [
													    ('This is a simple input 1', 'This is a simple input 2'),
													    ('This is a simple pair 1', 'This is a simple pair 2'),
													]

													# Simple input tests
													self.assertRaises(_SCREAMING_SNAKE_CASE     ,       tokenizer_r.encode     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'    )

													# Simple input
													self.assertRaises(_SCREAMING_SNAKE_CASE     ,       tokenizer_r.encode_plus     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'    )

													# Simple input
													self.assertRaises(
													    _SCREAMING_SNAKE_CASE     ,       tokenizer_r.batch_encode_plus     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'     ,       )

													# Pair input
													self.assertRaises(_SCREAMING_SNAKE_CASE     ,       tokenizer_r.encode     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'    )

													# Pair input
													self.assertRaises(_SCREAMING_SNAKE_CASE     ,       tokenizer_r.encode_plus     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'    )

													# Pair input
													self.assertRaises(
													    _SCREAMING_SNAKE_CASE     ,       tokenizer_r.batch_encode_plus     ,       _SCREAMING_SNAKE_CASE     ,       max_length=_SCREAMING_SNAKE_CASE     ,       padding='max_length'     ,       )







	def 			_lowerCAmelCase     (      self				:       str    )						->	Union[str, Any]:





					"""simple docstring"""


					pass




@require_ftfy
@require_spacy
@require_tokenizers
class     lowerCAmelCase__						(	_UpperCAmelCase ):


	'''simple docstring'''







	pass












"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






